Obesity is a highly heritable disease driven by complex interactions between genetic and environmental factors. Human genome-wide association studies (GWAS) have identiﬁed a number of loci contributing to obesity; however, a major limitation of these studies is the inability to assess environmental interactions common to obesity. Using a systems genetics approach, we measured obesity traits, global gene expression, and gut microbiota composition in response to a high-fat/high-sucrose (HF/HS) diet of more than 100 inbred strains of mice. Here we show that HF/HS feeding promotes robust, strain-speciﬁc changes in obesity that are not accounted for by food intake and provide evidence for a genetically determined set point for obesity. GWAS analysis identiﬁed 11 genome-wide signiﬁcant loci associated with obesity traits, several of which overlap with loci identiﬁed in human studies. We also show strong relationships between genotype and gut microbiota plasticity during HF/HS feeding and identify gut microbial phylotypes associated with obesity.

INTRODUCTION The dramatic increase in obesity during the past few decades is tightly associated with the increase in obesity-related complica-

tions, such as type 2 diabetes, heart disease, and cancer. Energy-rich diets containing high levels of fat and reﬁned carbohydrates along with sedentary lifestyles are believed to be the most signiﬁcant environmental factors contributing to this epidemic (Finucane et al., 2011; Malik et al., 2010). Understanding the genetic and environmental interactions contributing to obesity is thus crucial for developing novel therapies and preventive strategies. Human genome-wide association studies (GWAS) and studies of rare monogenic forms of obesity, as well as biochemical studies with cells and animal models, have identiﬁed relevant genes and pathways important in obesity; however, given the complexity of the obesity phenotype and the small amount of variance that can be explained by known obesity alleles, it is clear that much remains to be discovered (Attie and Scherer, 2009; Bouchard et al., 1990; Knights et al., 2011; Sandholt et al., 2010; Speakman et al., 2011). Obesity is strongly heritable in humans, with estimates ranging from 50% to 90% (Barsh et al., 2000; Stunkard et al., 1986). Large human GWAS explain less than 3% of this heritable component and environmental interactions with diet composi¨ tion likely add signiﬁcant complexity to such studies (Kilpelainen et al., 2011; Sonestedt et al., 2009; Speliotes et al., 2010). Indeed, long-term overfeeding in monozygotic twins promotes striking within-pair similarities in fat-mass gain, demonstrating that gene-by-diet interactions may be highly heritable and have a large impact on obesity (Bouchard et al., 1990). In addition to host genetic contributions, the microbial community within the gut has been shown to inﬂuence obesity in humans and mice (Turnbaugh et al., 2006, 2009a). The gut microbiota is a transmissable trait and can undergo dynamic population shifts with varied dietary composition (Benson et al., 2010; Turnbaugh et al., 2009b; Yatsunenko et al., 2012). Obese subjects have an
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altered gut microbiota compared to lean individuals, which may be an important contributing factor to the obesity epidemic (Turnbaugh et al., 2009a). To date, very little is known about the genetic basis of gene-by-diet and gut microbiota-diet interactions to common obesogenic factors, such as the consumption of energy-rich diets. Identiﬁcation of genes contributing to complex traits, such as obesity, in mice has been hampered by the poor mapping resolution of traditional genetic crosses (Bhatnagar et al., 2011; Burrage et al., 2010; Dokmanovic-Chouinard et al., 2008; Ehrich et al., 2005b; Flint et al., 2005; Lawson et al., 2011; York et al., 1999; Zhang et al., 1994). Based on the genome sequencing of many mouse strains, the identiﬁcation of millions of single-nucleotide polymorphisms (SNPs) (Keane et al., 2011), and the development of an algorithm that corrects for population structure in association analysis (Kang et al., 2008), we recently developed a systems genetics resource in the mouse capable of high-resolution genome-wide association mapping (Bennett et al., 2010). This resource, termed the hybrid mouse diversity panel (HMDP), is composed of more than 100 commercially available mouse strains and is ideal for systems-level analyses of gene-by-environment interactions. Association-based mapping approaches in rodents have recently been reviewed (Flint and Eskin, 2012). Employing a systems genetics approach in the mouse, we integrated physical traits, molecular traits, and gut microbiota composition data in response to an energy-rich diet. Using GWAS rather than quantitative trait locus (QTL) analyses, we obtained biologically meaningful genetic mapping, such that several of the genetic loci identiﬁed contained between one and three genes, comparable to human GWAS. These genes were prioritized with expression QTL (eQTL) analysis, and we were able to show a signiﬁcant overlap between mouse and human GWAS loci. We measured the change in fat dynamically, at ﬁve different points after high-fat/high-sucrose (HF/HS) feeding, providing strong evidence for a genetically controlled body fat set point. Our use of inbred mice strains also enabled detailed analysis of the relationship between gut microbiota composition, obesity traits, and diet. Overall, gene-by-diet interactions were highly reproducible and pervasive, providing a partial explanation for the failure of human studies to explain a larger fraction of the genetic basis of obesity. Our results indicate that mouse GWAS and systems genetics analyses provide a powerful method to complement human studies and to address factors, such as gene-by-diet interactions, that would be difﬁcult to study directly in humans. RESULTS Robust Variation in Gene-by-Diet Interactions For assessment of gene-by-diet interactions common to obesity, male mice were fed ad libitum a HF/HS diet that represents a typical fast food diet, in terms of fat and reﬁned carbohydrates (32% kcal from fat and 25% kcal from sucrose). Mice were maintained on a chow diet (6% kcal from fat) until 8 weeks of age and subsequently placed on a HF/HS diet for 8 weeks. Body fat percentage was assessed by magnetic resonance imaging (MRI) every 2 weeks, and food intake was monitored for a period of 1 week at the middle of the study timeline (study schematic shown in Figure 1A). Altogether, about 100 inbred
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strains of male mice were studied, with an average of six mice of each strain (Table S1 available online). A wide distribution in body fat percentage was observed in male mice before HF/HS feeding (Figures 1B). Dietary responses, as assessed by the body fat percentage increase during HF/HS feeding, varied widely among the strains (Figures 1C and S1). Although many strains exhibited a signiﬁcant increase in body fat percentage throughout the study timeline, their individual responses differed signiﬁcantly, from no change to a 200% increase in body fat percentage within the ﬁrst 2 weeks (Figures 1C). Most strains responded during the ﬁrst 4 weeks of HF/HS feeding and did not accumulate additional fat during the remainder of the study, suggesting an upper set point whereby continued body fat percentage growth is resisted (Figure 1C) (Speakman et al., 2011). The large effect of the HF/HS feeding on fat accumulation was conﬁrmed with age-matched (16-weekold) male mice (Table S2) fed a chow diet, which displayed similar body fat percentage to male mice before the HF/HS diet intervention (Figure S1A). Additionally, comparison of individual male strains maintained on a chow diet or fed a HF/HS diet for 8 weeks showed an average increase in body fat percentage from 0% to more than 600% (Figure S1B). We observed high heritability of about 80% for body fat percentage across the study timeline (Table 1). Changes in body fat percentage after HF/HS feeding were also highly heritable (>70%), suggesting that dietary responses are strongly controlled by genetics. Our results are consistent with the heritability estimates for body mass index (BMI) and obesity in humans (Barsh et al., 2000; Stunkard et al., 1986) and emphasize the importance of genetics in controlling obesity traits, such as gene-by-diet interactions. Factors Contributing to Dietary Responsiveness Overconsumption of high-calorie, energy-rich foods is a key environmental factor contributing to the global obesity epidemic (McCaffery et al., 2012). To understand the relationship between food intake and obesity, we monitored food intake, and we found it to range from 2–5 g per mouse per day. Total food intake per day was signiﬁcantly correlated with body weight and lean mass (Figures 1D and 1E). In contrast, body fat percentage and body fat percentage change after 4 weeks of HF/HS feeding showed little to no correlation with food intake (Figures 1F and 1G). This suggests that factors outside of food intake largely underlie the variation of obesity and fat mass gain between the strains in response to HF/HS feeding. To further deﬁne the contribution of energy consumption to the differences in fat accumulation, we performed in vivo metabolic chamber analyses of ﬁve inbred strains that are the progenitors of the recombinant inbred strains (and therefore contribute importantly to the overall genetic component) on chow diet. Signiﬁcant strain differences were observed in total food intake, activity, heat production, and utilization of different energy substrates, as indicated by respiratory exchange ratio (RER) (Figures S2A–S2D), all of which can inﬂuence dietary responses and subsequent fat accumulation. GWAS and Systems Genetics Analysis Association analysis was performed using about 100,000 informative SNPs, spaced throughout the genome, with efﬁcient

mixed model association (EMMA) adjusting for population structure (Kang et al., 2008). The threshold for genome-wide signiﬁcance was based on simulation and permutations, as previously described (Farber et al., 2011). This approach has been validated with transgenic analyses and by comparison with linkage analysis (Bennett et al., 2010). Altogether, 11 genome-wide signiﬁcant loci were found to be associated with obesity traits (Table 2). Loci averaged 500 kb to 2 Mb in size and in most cases contained 1 to 20 genes within a linkage disequilibrium (LD) block, an improvement of more than an order of magnitude as compared to traditional linkage analysis in mice which has a resolution of 10 to 20 Mb (Flint et al., 2005). In order to help identify candidate genes at loci, we carried out global expression analyses of epididymal adipose tissue in male mice (16 weeks old) fed a chow diet to determine genetic regulation and correlation between gene expression and body fat percentage. The loci controlling transcript levels in adipose tissue were mapped with EMMA and are referred to as expression quantitative trait loci (eQTL). Loci are termed ‘‘cis’’ if the locus maps within 1 Mb of the gene encoding the transcript

ants (Keane et al., 2011). Cbr1 reduces carbonyl compounds via NADPH-dependent oxidoreductase activity and through its glutathione (GSH)-binding site Cbr1 can act on GSH-conjugated molecules and play a role in controlling oxidative stress (Bateman et al., 2008), an important component of obesity (Furukawa et al., 2004). The loci for body fat growth after 8 weeks of HF/HS feeding also contained genes with known links to adipose biology and metabolism. The locus on chromosome 5 (rs13478388; p = 1.5 3 10À7) contains several C-X-C motif chemokine genes within LD. Cxcl5 is one of the genes in this cluster (Figure S4C), and expression of Cxcl5 is increased in adipose tissue of obese human subjects (Chavey et al., 2009). Cxcl5 contains nonsynonymous coding polymorphisms and an alternative splice site among multiple mouse strains (Keane et al., 2011), making Cxcl5 a strong candidate at the chromosome 5 locus. Similarly, the signiﬁcant SNP at chromosome 3 (rs29982345; p = 9.9 3 10À7) is within 1 Mb of three amylase genes, Amy2b, Amy2a5, and Amy1 (Figure S4D). Amylases play a critical role in the breakdown of polysaccharides to sugars, and targeting amylases has been suggested as a potential method for treating obesity (Barrett and Udani, 2011). The signiﬁcant peak on chromosome 11 (rs29417268; p = 2.8 3 10À7) has 11 genes within LD, and one gene, Glp2r, plays an important role in intestinal homeostasis and feeding behavior (Guan et al.,
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Conservation of Human and Mouse Metabolic Loci A comparison of the genes/loci identiﬁed in our study with GWAS genes for human obesity and BMI revealed signiﬁcant overlap (p = 4.0 3 10À4) (described in the Experimental Procedures). In addition to genome-wide signiﬁcant loci, we analyzed signiﬁcant human GWAS genes in our association results and identiﬁed suggestive associations near Opcml and Sorl1 (Table 3). Both Opcml and Sorl1 have been found in large human GWAS for visceral adipose/subcutaneous adipose ratio and waist circumference, respectively (Fox et al., 2012; Smith et al., 2010). Our results demonstrate that genes associated with obesity and BMI in humans show functional variation in mice and are associated with dietary interactions and obesity. Contributions of Gut Microbiota to Obesity A number of recent studies have implicated gut microbiota in metabolic and cardiovascular diseases (Turnbaugh et al., 2006; Vijay-Kumar et al., 2010; Wang et al., 2011). In order to investigate the genetic contributions to the gut microbiome and their relationship to obesity and dietary responses, we

examined the gut microbiota composition and distribution in strains after chow and HF/HS feeding. Since the microbiome is highly affected by environmental factors, all mice in the study were bred in the same facility and each strain was maintained in separate cages. We observed signiﬁcant phylum-level shifts in the gut microbiota composition after HF/HS feeding (Figure 3A). The shifts between the two dominant phyla Bacteroidetes and Firmicutes after HF/HS feeding (Figure 3A and Table S4) are consistent with previous reports (Turnbaugh et al., 2009b). Principal coordinates analysis (PCoA) of unweighted UniFrac (similar results for weighted UniFrac shown in Figure S6B) (Lozupone and Knight, 2005) distances detected two clear clusters corresponding to the two dietary conditions (Figure 3B), suggesting that HF/HS feeding dramatically alters gut microbial communities across a variety of genetic backgrounds. Compared to mice on a chow diet, the HF/HS-fed mice had greater abundance of several genera classiﬁed to order Clostridales in Phyla Firmicutes and lower abundances of Bacteroidetes, classiﬁed to family Porphyromonadaceae (41% chow versus 18% HF/HS, p = 4.36 3 10À11). In total we identiﬁed 17 genera whose abundance was signiﬁcantly changed by diet (p < 0.01) (Figure 3C and Table S4). Interestingly, the abundance of Akkermansia genera of the Verrucomicrobia phylum explains much of the unweighted UniFrac distance clusters on PCoA plots in HF/HS diet (Figures S6C and S6D). Robust effects of diet on the gut microbiota are well documented and recent evidence indicates that the gut microbiota constitute a complex polygenic trait under genetic regulation (Benson et al., 2010; McKnite et al., 2012; Qin et al., 2012). Consistent with the idea that the gut microbiota is inﬂuenced by genetic factors, we observed a strong effect of genetic background on the composition and plasticity of the gut microbiota after HF/HS feeding. Some strains showed large shifts in all major phyla, while other strains had little ﬂuctuation in their gut microbiota after HF/HS feeding (Figure 4A). Additionally, Procrustes analysis was used to compare the relative orientation of matched strains after HF/HS and chow feeding and conﬁrmed that the plasticity of gut microbial community is highly individualized (Figure 4B). Taken together, these results demonstrate a profound effect of HF/HS feeding on gut microbiota and show the strong inﬂuences of host genetics on inﬂuencing the plasticity of the gut microbiota in response to altered dietary compositions.

To test the relationship between obesity, dietary responsiveness, and gut microbiota composition, we analyzed all 11 genome-wide signiﬁcant loci with the abundances of speciﬁc gut phylotypes. Only the chromosome 3 locus (rs29982345), which is associated with body fat growth after 8 weeks of HF/ HS feeding showed a signiﬁcant enrichment (p = 0.0033) of the genera Enterobacteriaceae (phylum Proteobacteria). This region of chromosome 3, which contains three amylase genes, may therefore contribute to both dietary responsiveness and gut microbiota composition. We also examined the relationship between speciﬁc gut phylotypes and obesity traits and observed a modest but statistically signiﬁcant negative correlation with the abundance of the genera Akkermansia (phylum Verrucomicrobia) and body fat percentage growth after 8 weeks of HF/HS feeding (Figure 4C). Furthermore, body fat percentage growth after HF/ HS feeding was positively correlated with the relative abundances of Lactococcus from phylum Firmicutes (Figure 4D) and with the genera Allobaculum (phylum Bacteroidetes) (Figure 4E). Additional studies are warranted to validate the connections of these speciﬁc gut microbiota and dietary interactions. DISCUSSION Our results have led to several ﬁndings about the genetic control of responses to energy-rich diets consumed by an increasing fraction of the world’s population. Although biochemical and genetic studies have revealed important regulators of body fat, including feedback pathways that modulate food intake and energy expenditure, these studies have not revealed how common genetic variation controls these processes. Human GWAS have been successful at identifying many loci controlling measures such as body mass index, but they have been unable to examine gene-by-environment interactions. Classic linkage studies in mice, on the other hand, have been able to partially address issues such as dietary responsiveness (Ehrich et al., 2005b) but have been hindered by poor mapping resolution, and thus only a handful of genes have been identiﬁed (Buchner et al., 2012; Dokmanovic-Chouinard et al., 2008; Ehrich et al., 2005a; York et al., 1999; Zhang et al., 1994). Our results add to this body of knowledge in several important ways. First, the improved mapping resolution enabled us to identify several candidate genes for gene-by-environment interactions in obesity. Second, because we examined obesity traits in
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Figure 4. Plasticity of Gut Microbiota Is Strain Speciﬁc
(A) Gut Microbiota phyla shift in 52 strains after HF/HS feeding for 8 weeks indicated by shift in percent composition of indicated phylum. (B) Procrustes analysis of the same strains on chow diet and the HF/HS diet are linked with a bar. The unweighted UniFrac distances between the diets varies across strains, but are in general long, highlighting the shifts in microbial composition in response to diet. (C–E) Correlation of relative abundance of Akkermansia (A), Lactococcus (B), and Allobaculum (C) with body fat percentage increase from 0 to 8 weeks in male mice fed a HF/HS diet, regression line (red). r, biweight midcorrelation; p, p value. See also Figure S6.

a dynamic way, studying the mice after dietary challenge at multiple time points, we were able to provide strong evidence for the concept of a biologic set point. Third, we have integrated the obesity traits with global adipose transcript levels and gut microbiota composition. Fourth, our results emphasize the importance of gene-by-environment interactions, with important implications for an understanding of the overall genetic architecture of obesity. Finally, our results provide the basis of a systems genetics resource for obesity traits that can be expanded to include multiple biologic scales using metabalomics, proteomics, and epigenetics. In particular it will be of interest to examine behavioral and neurological differences among the strains as they relate to obesity traits. Using association rather than linkage, we were able to obtain biologically signiﬁcant mapping data, as several of the loci identiﬁed for the response to a HF/HS diet contained between one and three genes, comparable to human GWAS results. Altogether, we examined only about 800 mice, much fewer than the tens of thousands examined in human GWAS. It is likely that power is greatly increased in mice, as compared to humans, because of the ability to accurately monitor the phenotypes (body fat and food intake) and control the environment. Also,
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for the loci containing larger numbers of candidates, examination of transcript levels in adipose tissue and access to complete DNA sequence information allowed us to prioritize candidates. Indeed, several of the genes at such loci (Npc1 and Glp2r) have already been studied via transgenic approaches, with results consistent with our ﬁndings (Guan et al., 2012; Jelinek et al., 2010). Several different models have been developed to understand how genes and environment combine to regulate body fat. However, only limited studies have addressed the genetic basis of gene-by-environment interactions in obesity. The dual intervention point model postulates an upper and lower limit where genetic factors become dominant, and between which there is only weak genetic regulation (Speakman et al., 2011). Our results strongly support this concept, because after 4 weeks of HF/HS feeding most mouse strains reach an upper limit where there is no further increase in body fat (Figure 1). Thus, there must be strong genetic factors that resist continued fat accumulation beyond a certain point. Clearly, this set point varies widely in the population; some strains have already achieved the set-point level on a chow diet, whereas a few strains continue to accumulate fat throughout the 8 week feeding period, suggesting that

the set-point mechanism is disrupted. It is noteworthy that obesity prevalence appears to be leveling off in developed countries, which is also consistent with the idea of a set point (Flegal et al., 2012). Based on our data (Table 1) and a monozygotic twin study (Bouchard et al., 1990), acute body fat changes in response to an obesogenic environment are under strong genetic regulation, although this may be part of the same pathway that determines the set point. Our data also show that food intake is only modestly correlated with obesity and dietary responsiveness and indicates that energy expenditure may be a likely mechanism contributing to the obesity set point. Further metabolic studies of strains at the extremes will be required to conﬁrm the effect of energy expenditure on obesity set points. The experimental design of our study, involving inbred strains of mice, allowed us to study the gut microbiota composition of the mice both on a chow and HF/HS diet. The HF/HS diet clearly inﬂuenced gut microbiota composition in all of the 52 strains examined on both chow and the HF/HS diet (Figure 3). Shifts in gut microbiota composition and diversity in response to dietary changes are similar to those previously noted in both human (Ley et al., 2006; Turnbaugh et al., 2009b) and mouse studies (Ley et al., 2005; Turnbaugh et al., 2006). Our data indicate that host genetic factors inﬂuence gut microbiota plasticity in response to diet; however, since the gut microbiota can vary signiﬁcantly even in well-controlled cohorts we cannot exclude the possibility that other factors could partly explain the observed differences. We observed signiﬁcant relationships between gut microbiota and metabolic traits (Figures 4C–4E). The results are consistent with the concept that gut microbiota contribute to systemic functions and common diseases (Clemente et al., 2012). Future studies in the HMDP have the potential to test the roles of speciﬁc gut microbiota using transplantation and cross-fostering to systematically examine the relationship between genetic background, diet, and gut microbiota composition. Recent successes in human GWAS for common diseases have yielded many genetic loci, but in most instances they have revealed only a very small fraction of the estimated heritability (Lander, 2011). While it is likely that some of the ‘‘missing heritability’’ will be found in rare variants in the population, our results, and those from other studies in animal models (Burrage et al., 2010; Lawson et al., 2011; Shao et al., 2008) suggest that genetic interactions are likely to have important contributions. Clearly, human studies have limited ability to identify gene-byenvironment or gene-by-gene interactions (Zuk et al., 2012), and our results suggest that GWAS in mice will provide a powerful complement to human studies. A key feature of our experimental design is that genetically identical mice can be studied under multiple environmental conditions. Also, the greatly improved resolution of association as compared to classical linkage studies in mice greatly facilitates gene identiﬁcation and mechanistic understanding. The substantial overlap of our study with human GWAS for obesity traits suggests that at least for metabolic disorders, the mouse is highly relevant and that natural variations of the two species affect shared pathways. Because the inbred strains of the HMDP are permanent, data obtained about the strains is cumulative, allowing integration with previous results. The data compiled here provides a basis for further genetic studies of obesity-related traits in mice. It

will be of interest, for example, to examine additional intermediate phenotypes, such as protein and metabolite levels, in the same set of strains, and to relate these to the obesity traits using association mapping, correlation, and modeling. In this study, we examined transcript levels in adipose tissue and the composition of gut microbiota, and in the future it would be valuable to examine transcript levels in other relevant tissues, such as the hypothalamus, liver, and muscle, as well as protein, metabolite levels, and epigenetic markers. Moreover, traits such as epigenetic variation and gut microbiota composition can be tested for effects on obesity-related traits in this set of strains.
EXPERIMENTAL PROCEDURES Animals All mice were obtained from The Jackson Laboratory and were bred at University of California, Los Angeles to generate mice used in this study. Mice were maintained on a chow diet (Ralston Purina Company) until 8 weeks of age, when they were given a high-fat, high-sucrose diet (Research DietsD12266B) with the following composition: 16.8% kcal protein, 51.4% kcal carbohydrate, and 31.8% kcal fat. Age-matched control male mice were fed a chow diet (18% kcal fat) for 16 weeks. A complete list of the strains included in our study is included in Tables S1 and S2. The animal protocol for the study was approved by the Institutional Care and Use Committee (IACUC) at University of California, Los Angeles. Association Analysis and Heritability Calculations We performed the association testing of each SNP with a linear mixed model, which accounts for the population structure among the n animals using the following model (Kang et al., 2008): y = 1n m + xb + u + e; where m is the mean, b is the allele effect of the SNP, x is the (nx1) vector of observed genotypes of the SNP, u is the random effects due to genetic relatedness with varðuÞ = s2 K, and e is the random noise with varðeÞ = s2 I. K u e denotes the identity-by-state kinship matrix estimated from all the SNPs, I denotes the (nxn) identity matrix, and 1n is the (nx1) vector of ones. We estimated s2 and s2 using restricted maximum likelihood (REML) and computed u e p values using the standard F test to test the null hypothesis b = 0. Genomewide signiﬁcance threshold and genome-wide association mapping are determined as the family-wise error rate as the probability of observing one or more false positives across all SNPs for phenotype. We ran 100 different sets of permutation tests and parametric boot strapping of size 1,000 and observed that the mean and standard error of the genome-wide signiﬁcance threshold at the family-wise error rate of 0.05 were 3.9 3 10À6 ± 0.3 3 10À6, and 4.0 3 10À6 ± 0.3 3 10À6, respectively. A detailed explanation of the analyses is provided in Bennett et al. (2010). LD was determined by calculated pairwise r2 SNP correlations for each chromosome. Approximate LD boundaries were determined by visualizing r2 > 0.8 correlations in MATLAB (MathWorks). Heritability (in the narrow sense) is deﬁned as the phenotypic variance explained by additive genetic effects and is computed using the following model (Yang et al., 2010): y = 1n m + u + e and We estimated each trait as follows: s2 u s2 e using REML and calculated the heritability (h2) for

Assessment of Food Intake After 4 weeks of HF/HS feeding, food intake was monitored for 5 days. Ninety to one hundred grams of food was weighed and put in the food hopper, after 24 hr food was weighed, and then weighed again after 24 hr. The procedure was repeated again with fresh food, and a total of 4 3 24 hr period total food weight consumed was calculated by subtracting food weights; grams per mouse was then calculated by dividing average total food weight consumed in 4 3 24 hr periods by number of mice in cage. In Vivo Metabolism Assessment Metabolic rate, activity, food and water consumption were assessed with a Columbus Instruments Comprehensive Lab Animal Monitoring System (CLAMS; Columbus Instruments, Columbus, OH) equipped with subsystems for open circuit indirect calorimetry, and monitoring of activity, feeding, and drinking. Detailed methods for in vivo metabolism assessment provided in the Supplemental Experimental Procedures. Gut Microbiota Analysis Microbial community composition was assessed by pyrosequencing 16S rRNA genes derived from the cecal samples of chow diet and HF/HS diet animals. All mice used in the study were bred at UCLA to ensure uniform environmental conditions. Furthermore, strains were individually housed within the same vivarium throughout the duration of the study. To ensure comprehensive analysis, we sequenced biological replicates on all mice included in the study. Biological replicates were carried out on all mice included in the study. For mice fed a HF/HS diet, we sequenced a range of two to ﬁve mice per strain, with an average of three mice per strain (n = 339 male mice from 110 strains). Chow diet mice were sequenced by pooling of two to four mice per strain, with an average of two per strain (n = 108 male mice from 52 strains). We also sequenced technical replicates on eight mouse strains (ﬁve strains on chow diet and three on HF/HS diet) and observed high reproducibility of UniFrac analysis between technical replicates (Figure S6A). Detailed methods for gut microbiota analysis provided in the Supplemental Experimental Procedures. Adipose RNA Isolation and Global Gene Expression Analysis Flash frozen epididymal adipose samples from male mice (16 weeks old) maintained on a chow diet were weighed and homogenized in Qiazol (QIAGEN) and RNA isolated according to the manufacturer’s protocol with RNeasy columns (QIAGEN). Isolated RNA (two mice per strain as indicated in Table S2) was analyzed for global gene expression with Affymetrix HT_MG430A arrays and was ﬁltered as described (Bennett et al., 2010). Expression eQTL Analysis GWAS for gene expression was performed with EMMA and deﬁned as cis if peak SNP mapped within 1 Mb of gene position and trans if mapped outside (cis threshold, p < 1.4 3 10À3; trans threshold, p < 6.1 3 10À6). Statistics Correlations were calculated with the biweight midcorrelation, which is robust to outliers (Wilcox, 2005). Overlap p value between mouse and human GWAS genes was calculated with hypergeometric cumulative distribution function in MATLAB (MathWorks) with pval = 1 – hygecdf(x,M,K,N), where x is the total number of genes in genome, M is the total number of genes identiﬁed in mouse and humans, K s the number of genes identiﬁed in obesity and BMI human GWAS, and N is the number of genes identiﬁed in mouse obesity traits. ACCESSION NUMBERS All microarray data from this study are deposited in the NCBI GEO database (http://www.ncbi.nlm.nih.gov/geo/) under the accession number GSE42890. SUPPLEMENTAL INFORMATION Supplemental Information includes Supplemental Experimental Procedures, six ﬁgures, and four tables and can be found with this article online at http:// dx.doi.org/10.1016/j.cmet.2012.12.007.